Preface

Whether you are reading this book as a computational biologist or a Python programmer, you will probably relate to the phrase "explosive growth, exciting times." The recent growth in the use of Python is strongly connected with its status as big data's main programming language. The deluge of data in biology, mostly from genomics and proteomics, makes bioinformatics one of the forefront applications of data science. There is a massive need for bioinformaticians to analyze all this data; of course, one of the main tools is Python. We will not only talk about the programming language but also the whole community and software ecology behind it.

When you choose Python to analyze your data, you expect to get an extensive set of libraries, ranging from statistical analysis to plotting, parallel programming, machine learning, and bioinformatics. However, you actually get even more than this; the community has a tradition of providing good documentation, reliable libraries, and frameworks. It is also friendly and supportive of all its participants.

In this book, we will present practical solutions to modern bioinformatics problems using Python. Our approach will be hands-on; we will address important topics, such as next-generation sequencing, genomics, population genetics, phylogenetics, and proteomics.

At this stage, you probably know the language reasonably well and are aware of the basic analysis methods in your field of research. You will dive directly into relevant complex computational biology problems and learn how to tackle them with Python. This is not your first Python book or your first biology lesson; this is where you will find reliable and pragmatic solutions to realistic and complex problems.

The first edition of this book took several high-risk decisions a few years ago, considering Docker, Jupyter Notebook, and even Python 3 were not obvious choices. These choices worked perfectly well. The second edition once again uses these technologies, which are now standard in the field. Probably due to bioinformatics being a more mature field, there are no high-risk options now. There is new content on pipelines, parallel processing systems, and file formats, but none of them are unsafe bets.